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With increased use of H.264/AVC in various applications including video surveillance systems, feature extraction and knowledge representation in compressed domain are becoming attractive. A real-time H.264/AVC compressed domain moving object segmentation and tracking algorithm for surveillance videos is proposed in this paper. This algorithm consists of moving object detection, bounding box matching, spatiotemporal merge and split reasoning and trajectory smoothing, with major innovation in incorporating the information provided by the prediction modes into the framework of motion detection and trajectory construction. The experimental results on both indoor and outdoor surveillance videos demonstrate that the adaptive use of the information from motion vectors, DCT coefficients and prediction modes can substantially improve the performance of moving object segmentation and tracking.